F. William Townes

This study uses longitudinal single-cell data to show changes in monocyte gene expression and metabolic state after ASCT in jSSc patients.

This paper introduces a TBLDA framework to identify eQTLs across tissues using raw count data with a one-to-many sample-to-genotype mapping.

This paper introduces scTBLDA, a method for identifying eQTLs in single-cell RNA-seq data without relying on cell-type labels, and applies it to find cis- and trans-eQTLs in lupus patients.

This paper introduces a probabilistic model to align spatial genomics and histology data across different scales and technologies, enabling accurate downstream analysis.

This paper investigates cerebellar contributions to reversal learning by mapping brainwide networks through c-Fos activation and identifies distinct effects of lobule VI and crus I inactivation.

The paper introduces a framework for region-based association testing in family-based studies, showing improved performance over existing methods in simulations and a real dataset.

The paper compares classification algorithms to predict pro- and anti-longevity genes using gene expression and ontology data in C. elegans and S. cerevisiae, finding elastic net logistic regression performs best.

This paper introduces quasi-UMIs for normalizing scRNA-seq data without UMIs, improving accuracy over existing methods.

The paper introduces multinomial-based methods for feature selection and dimension reduction in scRNA-Seq, improving clustering performance over existing approaches.

The paper examines technical variability and missing data in scRNA-seq, showing that technical zeros and cell-to-cell variation can confound biological findings.

This paper introduces a matrix factorization method that accounts for censored data in scRNA-Seq to improve clustering accuracy.